Relationship between the unique microstructures and behaviors of high-entropy alloys

High-entropy alloys(HEAs),which were introduced as a pioneering concept in 2004,have captured the keen interest of nu-merous researchers.Entropy,in this context,can be perceived as representing disorder and randomness.By contrast,elemental composi-tions within alloy systems occupy specific structural sites in space,a concept referred to as structure.In accordance with Shannon entropy,structure is analogous to information.Generally,the arrangement of atoms within a material,termed its structure,plays a pivotal role in dictating its properties.In addition to expanding the array of options for alloy composites,HEAs afford ample opportunities for diverse structural designs.The profound influence of distinct structural features on the exceptional behaviors of alloys is underscored by numer-ous examples.These features include remarkably high fracture strength with excellent ductility,antiballistic capability,exceptional radi-ation resistance,and corrosion resistance.In this paper,we delve into various unique material structures and properties while elucidating the intricate relationship between structure and performance.

TiAl合金表面Al-Y渗层的抗热腐蚀性能

针对TiAl合金抗热腐蚀性能不足的问题,采用扩散渗法在其表面制备了Al-Y渗层,研究了催化剂类型对渗层组织结构的影响,分析了渗层的热冲击性能,对比研究了TiAl基体和Al-Y渗层在25%NaCl+75%Na2SO4熔盐(质量分数wt.%)中的热腐蚀行为。结果表明:用不同催化剂制备的Al-Y渗层具有相似的结构,与基体均为良好的冶金结合,由外向内均由富Al外层、TiAl_(3)中间层和TiAl_(2)内层构成,但当采用NH_(4)Cl为催化剂时,渗层的致密度和均匀性较用NaF和AlCl_(3)∙6H_(2)O催化剂好。在1000℃热冲击下,Al-Y渗层较基体合金具有更强的抗热冲击性能。TiAl合金在热腐蚀时,基体中的片层状α_(2)-Ti_(3)Al相首先与O、S介质发生选择性腐蚀,随后形成灾难性腐蚀;而Al-Y涂层在热腐蚀初期会形成致密的Al_(2)O_(3)氧化膜,有效地保护了渗层,随着热腐蚀时间的延长,由于渗层内原子的内外扩散和腐蚀应力,渗层逐渐开裂,裂纹成为O和S元素的扩散通道,但在此过程中,渗层中的TiAl_(3)相发生分解,为裂纹部位提供Al源并形成Al_(2)O_(3)来填补裂纹,延缓了O和S原子的内扩散速率,增强了TiAl合金的耐热腐蚀性能。

User Purchase Intention Prediction Based on Improved Deep Forest

Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection.To address this issue,based on the deep forest algorithm and further integrating evolutionary ensemble learning methods,this paper proposes a novel Deep Adaptive Evolutionary Ensemble(DAEE)model.This model introduces model diversity into the cascade layer,allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns.Moreover,this paper optimizes the methods of obtaining feature vectors,enhancement vectors,and prediction results within the deep forest algorithm to enhance the model’s predictive accuracy.Results demonstrate that the improved deep forest model not only possesses higher robustness but also shows an increase of 5.02%in AUC value compared to the baseline model.Furthermore,its training runtime speed is 6 times faster than that of deep models,and compared to other improved models,its accuracy has been enhanced by 0.9%.

MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot.However,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper.The main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale features.The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively.Secondly,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature information.Thirdly,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different directions.To verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried out.In the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,respectively.In the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,respectively.This model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

Effects of long-term fencing on soil microbial community structure and function in the desert steppe,China

One of the goals of grazing management in the desert steppe is to improve its ecosystem.However,relatively little is known about soil microbe communities in the desert steppe ecosystem under grazing management.In this study,we investigated the diversity and aboveground biomass of Caragana korshinskii Kom.shrub communities in long-term fencing and grazing areas,combined with an analysis of soil physical-chemical properties and genomics,with the aim of understanding how fence management affects plant-soil-microbial inter-relationships in the desert steppe,China.The results showed that fence management(exclosure)increased plant diversity and aboveground biomass in C.korshinskii shrub area and effectively enhanced soil organic carbon(233.94%),available nitrogen(87.77%),and available phosphorus(53.67%)contents.As well,the Shannon indices of soil bacteria and fungi were greater in the fenced plot.Plant-soil changes profoundly affected the alpha-and beta-diversity of soil bacteria.Fence management also altered the soil microbial community structure,significantly increasing the relative abundances of Acidobacteriota(5.31%-8.99%),Chloroflexi(3.99%-5.58%),and Glomeromycota(1.37%-3.28%).The soil bacterial-fungal co-occurrence networks under fence management had higher complexity and connectivity.Based on functional predictions,fence management significantly increased the relative abundance of bacteria with nitrification and nitrate reduction functions and decreased the relative abundance of bacteria with nitrate and nitrite respiration functions.The relative abundances of ecologically functional fungi with arbuscular mycorrhizal fungi,ectomycorrhizal fungi,and saprotrophs also significantly increased under fence management.In addition,the differential functional groups of bacteria and fungi were closely related to plant-soil changes.The results of this study have significant positive implications for the ecological restoration and reconstruction of dry desert steppe and similar areas.

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.

Dioscin from Polygonatum sibiricum induces apoptosis and autophagy in Ishikawa human endometrial cancer cell and in vivo

With an aim to comprehend the precise regulatory mechanism of dioscin against endometrial carcinoma(EC), we firstly extracted the components from Polygonatum sibiricum followed by identification and structural characterization. The anti-EC activity of dioscin was initially determined based on the inhibition of Ishikawa cell proliferation and tumor growth. The high-throughput sequencing data indicated that dioscin not only promoted apoptosis, including decrease of poly ADP-ribose polymerase(PARP) and B-cell lymphoma-2(Bcl-2) and increase of c-PARP and Bcl-2-associcated agonist of cell death(Bad), but also induced autophagy, including increase of autophagic lysosomes and LC3Ⅱ/LC3Ⅰ ratio. Mechanistic exploration suggested that dioscin induced autophagy and apoptosis through inhibition of PI3K/AKT/mTOR signaling pathway. Besides, the dioscin-regulated p53 pathway was mainly involved in autophagy induction. Furthermore, inhibition of Ishikawa cell autophagy was linked to dioscin-induced apoptosis. Our data suggest the immense potential of dioscin for the development of functional food for EC and related medical application.

Recommendation Method for Contrastive Enhancement of Neighborhood Information

Knowledge graph can assist in improving recommendation performance and is widely applied in various person-alized recommendation domains.However,existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph.To tackle these issues,this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise.Specifically,first,this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items,mining the high-order neighbor informa-tion of users and items.Next,in the neighbor information,this paper introduces weak noise following a uniform distribution to construct neighbor contrast views,effectively reducing the time overhead of view construction.This paper then performs contrastive learning between neighbor views to promote the uniformity of view information,adjusting the neighbor structure,and achieving the goal of reducing the knowledge noise in the knowledge graph.Finally,this paper introduces multi-task learning to mitigate the problem of weak supervisory signals.To validate the effectiveness of our method,experiments are conducted on theMovieLens-1M,MovieLens-20M,Book-Crossing,and Last-FM datasets.The results showthat compared to the best baselines,our method shows significant improvements in AUC and F1.

L_(1)-Smooth SVM with Distributed Adaptive Proximal Stochastic Gradient Descent with Momentum for Fast Brain Tumor Detection

Brain tumors come in various types,each with distinct characteristics and treatment approaches,making manual detection a time-consuming and potentially ambiguous process.Brain tumor detection is a valuable tool for gaining a deeper understanding of tumors and improving treatment outcomes.Machine learning models have become key players in automating brain tumor detection.Gradient descent methods are the mainstream algorithms for solving machine learning models.In this paper,we propose a novel distributed proximal stochastic gradient descent approach to solve the L_(1)-Smooth Support Vector Machine(SVM)classifier for brain tumor detection.Firstly,the smooth hinge loss is introduced to be used as the loss function of SVM.It avoids the issue of nondifferentiability at the zero point encountered by the traditional hinge loss function during gradient descent optimization.Secondly,the L_(1) regularization method is employed to sparsify features and enhance the robustness of the model.Finally,adaptive proximal stochastic gradient descent(PGD)with momentum,and distributed adaptive PGDwithmomentum(DPGD)are proposed and applied to the L_(1)-Smooth SVM.Distributed computing is crucial in large-scale data analysis,with its value manifested in extending algorithms to distributed clusters,thus enabling more efficient processing ofmassive amounts of data.The DPGD algorithm leverages Spark,enabling full utilization of the computer’s multi-core resources.Due to its sparsity induced by L_(1) regularization on parameters,it exhibits significantly accelerated convergence speed.From the perspective of loss reduction,DPGD converges faster than PGD.The experimental results show that adaptive PGD withmomentumand its variants have achieved cutting-edge accuracy and efficiency in brain tumor detection.Frompre-trained models,both the PGD andDPGD outperform other models,boasting an accuracy of 95.21%.

MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion.Such as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and PET-CT.How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions.To solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this paper.Firstly,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate lesions.Secondly,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after downsampling.Thirdly,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic information.Finally,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local attention.The proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,respectively.It is of great significance for computer-aided diagnosis.

Rule acquisition of three-way semi-concept lattices in formal decision context

Three-way concept analysis is an important tool for information processing,and rule acquisition is one of the research hotspots of three-way concept analysis.However,compared with three-way concept lattices,three-way semi-concept lattices have three-way operators with weaker constraints,which can generate more concepts.In this article,the problem of rule acquisition for three-way semi-concept lattices is discussed in general.The authors construct the finer relation of three-way semi-concept lattices,and propose a method of rule acquisition for three-way semi-concept lattices.The authors also discuss the set of decision rules and the relationships of decision rules among object-induced three-way semi-concept lattices,object-induced three-way concept lattices,classical concept lattices and semi-concept lattices.Finally,examples are provided to illustrate the validity of our conclusions.

Colorectal Cancer Segmentation Algorithm Based on Deep Features from Enhanced CT Images

Colorectal cancer,a malignant lesion of the intestines,significantly affects human health and life,emphasizing the necessity of early detection and treatment.Accurate segmentation of colorectal cancer regions directly impacts subsequent staging,treatment methods,and prognostic outcomes.While colonoscopy is an effective method for detecting colorectal cancer,its data collection approach can cause patient discomfort.To address this,current research utilizes Computed Tomography(CT)imaging;however,conventional CT images only capture transient states,lacking sufficient representational capability to precisely locate colorectal cancer.This study utilizes enhanced CT images,constructing a deep feature network from the arterial,portal venous,and delay phases to simulate the physician’s diagnostic process and achieve accurate cancer segmentation.The innovations include:1)Utilizing portal venous phase CT images to introduce a context-aware multi-scale aggregation module for preliminary shape extraction of colorectal cancer.2)Building an image sequence based on arterial and delay phases,transforming the cancer segmentation issue into an anomaly detection problem,establishing a pixel-pairing strategy,and proposing a colorectal cancer segmentation algorithm using a Siamese network.Experiments with 84 clinical cases of colorectal cancer enhanced CT data demonstrated an Area Overlap Measure of 0.90,significantly better than Fully Convolutional Networks(FCNs)at 0.20.Future research will explore the relationship between conventional and enhanced CT to further reduce segmentation time and improve accuracy.

Structure and electrical properties of polysilicon films doped with ammonium tetraborate tetrahydrate

Here,p-type polysilicon films are fabricated by ex-situ doping method with ammonium tetraborate tetrahydrate(ATT)as the boron source,named ATT-pPoly.The effects of ATT on the properties of polysilicon films are comprehensively analyzed.The Raman spectra reveal that the ATT-pPoly film is composed of grain boundary and crystalline regions.The preferred orientation is the(111)direction.The grain size increases from 16−23 nm to 21−47 nm,by~70%on average.Comparing with other reported films,Hall measurements reveal that the ATT-pPoly film has a higher carrier concentration(>10^(20)cm^(−3))and higher carrier mobility(>30 cm2/(V·s)).The superior properties of the ATT-pPoly film are attributed to the heavy doping and improved grain size.Heavy doping property is proved by the mean sheet resistance(Rsheet,m)and distribution profile.The R_(sheet,m)decreases by more than 30%,and it can be further decreased by 90%if the annealing temperature or duration is increased.The boron concentration of ATT-pPoly film annealed at 950℃ for 45 min is~3×10^(20)cm^(−3),and the distribution is nearly the same,except near the surface.Besides,the standard deviation coefficient(σ)of Rsheet,m is less than 5.0%,which verifies the excellent uniformity of ATT-pPoly film.

Enhancing the Electrocatalytic Oxidation of 5-Hydroxymethylfurfural Through Cascade Structure Tuning for Highly Stable Biomass Upgrading

Electrocatalytic 5-hydroxymethylfurfural oxidation reaction(HMFOR)provides a promising strategy to convert biomass derivative to highvalue-added chemicals.Herein,a cascade strategy is proposed to construct Pd-NiCo_(2)O_(4)electrocatalyst by Pd loading on Ni-doped Co3O4 and for highly active and stable synergistic HMF oxidation.An elevated current density of 800 mA cm^(-2)can be achieved at 1.5 V,and both Faradaic efficiency and yield of 2,5-furandicarboxylic acid remained close to 100%over 10 consecutive electrolysis.Experimental and theoretical results unveil that the introduction of Pd atoms can modulate the local electronic structure of Ni/Co,which not only balances the competitive adsorption of HMF and OH-species,but also promote the active Ni^(3+)species formation,inducing high indirect oxidation activity.We have also discovered that Ni incorporation facilitates the Co2+pre-oxidation and electrophilic OH*generation to contribute direct oxidation process.This work provides a new approach to design advanced electrocatalyst for biomass upgrading.

Egg rejection and egg recognition mechanism in a Chinese Azure-winged Magpie(Cyanopica cyanus)population

Recognition and rejection of foreign eggs are effective defense of hosts against brood parasitism.However,brood parasitism can impose various selection pressures on different geographic populations of the same host species.In a multiple cuckoo system in China,Azure-winged Magpies(Cyanopica cyanus)are parasitized by both Indian Cuckoos(Cuculus micropterus)and Asian Koels(Eudynamys scolopaceus).In this study,egg recognition ability and recognition mechanism of the Azure-winged Magpie were investigated using a population in Fusong,southeastern Jilin,China.The results showed that 55.6%(20/36)of the Azure-winged Magpies correctly rejected quail(Coturnix japonica)eggs in their nests,while 13.9%(5/36)of the individuals experienced rejection costs by wrongly rejecting their own eggs.Azure-winged Magpies could accurately reject the experimental eggs when the number of such eggs in the nests was the same as that of the magpie eggs.However,Azure-winged Magpies do not recognize and reject conspecific eggs(0/28).The present study indicates that the Azure-winged Magpie has moderate egg recognition ability toward non-mimetic quail eggs and shows a true recognition mechanism with rejecting foreign eggs by accurately recognizing their own eggs.However,they cannot recognize conspecific eggs.

Apigenin, a natural flavonoid, promotes autophagy and ferroptosis in human endometrial carcinoma Ishikawa cells in vitro and in vivo

Apigenin,a natural flavonoid has been reported against a variety of cancer types.However,it is unclear whether apigenin can promote autophagy and ferroptosis in Ishikawa cells.There are few reports on the mechanism of apigenin on autophagy and ferroptosis of endometrial cancer Ishikawa cells.We found that iron accumulation,lipid peroxidation,glutathione consumption,p62,HMOX1,and ferritin were increased,while,solute carrier family 7 member 11 and glutathione peroxidase 4 were decreased.Ferrostatin-1,an iron-death inhibitor could reverse the effects of apigenin in Ishikawa cells.On the other hand,apigenin could promote autophagy via up-regulating Beclin 1,ULK1,ATG5,ATG13,and LC3B and down-regulating AMPK,mTOR,P70S6K,and ATG4.Furthermore,apigenin could inhibit tumor tissue proliferation and restrict tumor growth via ferroptosis in vivo.

The anti-cancerous mechanism of licochalcone A on human hepatoma cell HepG2 based on the miRNA omics

To explore the function of licochalcone A as an anticancer phytochemical on HepG2 cells and investigate its potential mechanisms,we analyzed the microRNAs(miRNAs)expression profile of HepG2 cells in response to licochalcone A(70μmol/L)in vitro.102 dysregulated miRNAs were detected,and SP1 was expected as the transcription factor that regulates the functions of most screened miRNAs.A sum of 431 targets,the overlap of predicted mRNAs from TargetScan,miRDB,and miRtarbase were detected as the targets for these dysregulated miRNAs.FoxO signaling pathway was the hub pathway for the targets.A protein-protein interaction network was structured on the STRING platform to discover the hub genes.Among them,PIK3R1,CDC42,ESR1,SMAD4,SUMO1,KRAS,AGO1,etc.were screened out.Afterwards,the miRNA-target networks were established to screen key dysregulated miRNAs.Two key miRNAs(hsa-miR-133b and hsa-miR-145-5p)were filtered.Finally,the miRNA-target-transcription factor networks were constructed for these key miRNAs.The networks for these key miRNAs included three and two transcription factors,respectively.These identified miRNAs,transcription factors,targets,and regulatory networks may offer hints to understand the molecular mechanism of licochalcone A as a natural anticarcinogen.

Hopf bifurcation of nonlinear system with multisource stochastic factors

The article mainly explores the Hopf bifurcation of a kind of nonlinear system with Gaussian white noise excitation and bounded random parameter.Firstly,the nonlinear system with multisource stochastic fac-tors is reduced to an equivalent deterministic nonlinear system by the sequential orthogonal decomposi-tion method and the Karhunen-Loeve(K-L)decomposition theory.Secondly,the critical conditions about the Hopf bifurcation of the equivalent deterministic system are obtained.At the same time the influence of multisource stochastic factors on the Hopf bifurcation for the proposed system is explored.Finally,the theorical results are verified by the numerical simulations.